Application of Supervised Principal Motion Analysis to Evaluate Subjectively Easy Sit-to-Stand Motion of Healthy People

Qiu, Chongyang; Okamoto, Shogo; Akiyama, Yasuhiro; Yamada, Yoji

doi:10.1109/access.2021.3078202

Cited by 3 publications

(3 citation statements)

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“…We employ PMA [28] to estimate the MoS values from the triaxial translational velocities of a body feature. PMA encompasses a suite of linear time-series data analysis techniques.…”

Section: Principal Motion Analysis To Estimate Mos Values From Kinema...mentioning

confidence: 99%

“…The unsupervised variant, known as PMA0, extends the principal component analysis to time-series data and has been applied in the analysis of human motion [35][36][37][38][39]. For this study, we used a supervised variant, PMA1 [28], which is a time-series extension of partial least-squares regression analysis. In PMA1, a time-series data sample is represented as a linear combination of multiple independent time-series bases.…”

Section: Principal Motion Analysis To Estimate Mos Values From Kinema...mentioning

confidence: 99%

“…We compared 10 body feature points in terms of the accuracy of the anterior and mediolateral MoSs. To this end, a principal motion analysis (PMA) [26,28] has been conducted using triaxial translational velocities. This study builds upon our previous study [29], which compared three body points, including seven additional feature points, using nuanced statistical methods, specifically cross-validation.…”

Section: Introductionmentioning

confidence: 99%

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Estimating the Margin of Gait Stability in Healthy Elderly Using the Triaxial Kinematic Motion of a Single Body Feature

Liu,

Okamoto,

Kuroda

et al. 2024

Applied Sciences

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Gait stability indices are crucial for identifying individuals at risk of falling while walking. The margin of stability is one such index, known for its good construct validity. Generally, the measurement of this stability index requires a motion capture system, rendering it inaccessible for everyday use. This study proposes an alternative approach by estimating the index through time-series data of triaxial kinematic motion from a single body feature. We analyzed an open gait database comprising data from 60 participants aged over 60 to identify the most accurate body feature for estimating the margin of stability. The margin of stability values were estimated by using principal motion analysis, with the time series of the triaxial translational velocities of a body feature as predictors. Among the 10 body feature points, the sacral crest provided the highest accuracy, with the correlation coefficients between observation and estimation being 0.56 and 0.54 for the mediolateral and anterior directions, respectively. Although these values need to be further improved, these findings pave the way for developing an accessible system to estimate fall risks.

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“…We employ PMA [28] to estimate the MoS values from the triaxial translational velocities of a body feature. PMA encompasses a suite of linear time-series data analysis techniques.…”

Section: Principal Motion Analysis To Estimate Mos Values From Kinema...mentioning

confidence: 99%